Search Results - "sparse stacked denoising autoencoder"
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A sparse stacked denoising autoencoder with optimized transfer learning applied to the fault diagnosis of rolling bearings
ISSN: 0263-2241, 1873-412XPublished: London Elsevier Ltd 01.11.2019Published in Measurement : journal of the International Measurement Confederation (01.11.2019)“…•Fault diagnosis by stacked autoencoder requires less professional knowledge.•Domain adaptation by optimized transfer learning reduces the algorithm’s…”
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Network intrusion detection based on Contractive Sparse Stacked Denoising Autoencoder
ISSN: 2155-5052Published: IEEE 04.08.2021Published in IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (04.08.2021)“…The rapid growth of network scale leads to the increasingly prominent network security problems. Intrusion detection is an important method to resist complex…”
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Conference Proceeding -
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A novel multimodal framework for automatic recognition of individual cattle based on hybrid features using sparse stacked denoising autoencoder and group sparse representation techniques
ISSN: 1380-7501, 1573-7721Published: New York Springer US 01.09.2022Published in Multimedia tools and applications (01.09.2022)“… This paper proposes an automatic recognition system to identify cattle based on face and muzzle image features using sparse Stacked Denoising Autoencoder (SDAE…”
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A Nonnegativity-Constraint Sparse Stacked Denoising Autoencoder for Anomaly Detection in Electric Power Communication Network
ISSN: 2155-5052Published: IEEE 27.10.2020Published in IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (27.10.2020)“… To deal with this problem, a nonnegativity-constraint sparse stacked denoising autoencoder…”
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Conference Proceeding -
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Adaptive Diagnosis for Transformer with Unknown Faults Based on Antenna-Augmented RFID Sensor and Deep Learning
ISSN: 1530-437X, 1558-1748Published: New York IEEE 01.09.2023Published in IEEE sensors journal (01.09.2023)“… Therefore, an adaptive fault diagnosis method consisting of sparse stacked denoising autoencoder (SSDA…”
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An Anomaly Detection Method for Nonlinear Industrial Process Using Sparse Stacked Denoising Autoencoder
Published: IEEE 16.12.2023Published in 2023 9th International Conference on Systems and Informatics (ICSAI) (16.12.2023)“…With the increasing complexity of industrial systems, new challenges are posed to the monitoring of industrial process data, which often appear to be…”
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Conference Proceeding -
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Blind Denoising Autoencoder
ISSN: 2162-237X, 2162-2388, 2162-2388Published: United States IEEE 01.01.2019Published in IEEE transaction on neural networks and learning systems (01.01.2019)“…The term "blind denoising" refers to the fact that the basis used for denoising is learned from the noisy sample itself during denoising. Dictionary learning-…”
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Learning social representations with deep autoencoder for recommender system
ISSN: 1386-145X, 1573-1413Published: New York Springer US 01.07.2020Published in World wide web (Bussum) (01.07.2020)“…With the development of online social media, it attracts increasingly attentions to utilize social information for recommender systems. Based on the intuition…”
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Blind Denoising Autoencoder
ISSN: 2331-8422Published: Ithaca Cornell University Library, arXiv.org 11.12.2019Published in arXiv.org (11.12.2019)“…The term blind denoising refers to the fact that the basis used for denoising is learnt from the noisy sample itself during denoising. Dictionary learning and…”
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Predicting non-attendance in hospital outpatient appointments using deep learning approach
ISSN: 2047-6965, 2047-6973Published: Taylor & Francis 03.07.2022Published in Health Systems (03.07.2022)“… prediction model based on deep neural networks and machine learning models. The proposed approach works upon sparse stacked denoising autoencoders (SDAEs…”
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Regularized Deep Learning for Face Recognition With Weight Variations
ISSN: 2169-3536, 2169-3536Published: Piscataway IEEE 01.01.2015Published in IEEE access (01.01.2015)“… In this paper, we propose a regularizer-based approach to learn weight invariant facial representations using two different deep learning architectures, namely, sparse-stacked denoising autoencoders…”
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